It's Alive : An Examination of Brand Humanization on Lululemon's Instagram
Bibliographic record
Abstract
In an increasingly saturated market economy, creating a ‘human’ brand is imperative to the sustained salience of a company over time. Today and in prior years, successful brands are able to foster a relationship with consumers that resembles one between humans— at times influencing consumers’ anthropomorphized perception of brands. Online media have changed the way humans are perceived as being ‘present’ and ‘real’ in digital contexts, forcing brands to adapt their effort to create affective and humanized content. Through a case study of Lululemon Athletica, this Major Research Paper (MRP) examines how empirically tested strategies from brand humanization research apply to a previously under-examined domain— Instagram. The latter half of this study examines how user comments on Lululemon’s Instagram platform reflect a human-like brand-consumer relationship bond— that which may, in some ways, reflect an anthropomorphized perception of Lululemon. While the aim of this study is not to prove a humanized perception of Lululemon, the quality of engagement in posts’ comments frequently mirrors dialogue that would occur between humans, and further mirrors a human-like relational bond between the consumer and brand. Findings emphasize the value in brands depicting meaningful identity narratives that represent ‘deeper’ beliefs and values, engaging users in an interactive dialogue, and portraying relatable human experiences for users to identify with, internalize, and aspire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".